Computerized method and system for inferring genetic findings for a patient

ABSTRACT

A method and system in a computing environment for inferring genetic findings for a patient is provided. The method includes receiving a request for genetic findings for a person from another application or a user. The method further includes inquiring as to whether the person has the genetic findings. If not, the method automatically provides inferred genetic findings for the person. The inferred genetic findings are calculated using genetic findings for family members of the patient, linkage analysis, haplotype analysis, semantic test results for the person and/or population genetics information.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. ProvisionalApplication No. 60/509,023 filed on Oct. 6, 2003.

The present application is a continuation-in-part of co-pending U.S.application Ser. No. 09/981,248 filed Oct. 16, 2001, entitled COMPUTERSYSTEM FOR PROVIDING INFORMATION ABOUT THE RISK OF AN ATYPICAL CLINICALEVENT BASED UPON GENETIC INFORMATION.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

TECHNICAL FIELD

The present invention relates generally to the field of computersoftware. More particularly, the invention relates to a method andsystem for inferring genetic findings for a patient.

BACKGROUND OF THE INVENTION

In recent years, genetic information has become increasingly availablethrough research efforts such as the Human Genome Project. However,genetic information has not been incorporated effectively into theclinical decision making process. Genetic specialists have been amongthe first to incorporate this new information into their practice.However, non-specialists, such as general practitioners, pediatricians,surgeons and pharmacists, also need to improve their practices toreflect recent advances in genetics research even though they may lack adeep understanding of genetics.

As more genetic information has become available, technical advanceshave led to affordable genetic testing for many relevant geneticmutations. Nonetheless, most individuals have not undergone genetictesting and widespread use of genetic testing is likely to take five toten years. Thus, for an individual patient, result values for mutationsin a particular gene relevant to that patient's treatment may not alwaysbe available. However, genetic findings for one or more family membersof the individual may be available. Demographic information and geneticfindings for linked genes may also be available. While this informationcan be used to infer a genetic test result, it has not yet beenintegrated into an effective clinical process for the non-expert to usein the clinical decision making process.

Existing programs for inferring genetic finds are ineffective for anumber of reasons. These programs require a user actively to solicit aninference. Once the user deliberately launches one of the existingprograms, the program requires the user to complete a family tree byasking the user to indicate medical conditions known for each individualin the family tree. The user then selects an individual(s) in the familytree and the program returns a prediction. Thus, in these programs, theuser must launch the program and specifically request a prediction for aparticular person. Since the programs are not integrated into a unifiedhealthcare information technology system, information about a person'sfamily must be input manually. While the relationships are relativelysimple, the genetic information is oftentimes difficult to understandand input into the system. As such, these programs are used byindividuals with significant training and expertise in genetics. Evenwith skilled operators, the opportunities for human error aresignificant and the consequences of such errors is oftentimes great.

Accordingly, there is a need for an effective system and method forincorporating genetic information about the family of a patient into theclinical decision making process for the beneficial use by thenon-expert. A need also exists for a system capable of inferring geneticfindings for a patient when such a result would be useful in thedecision making process but is absent for the patient being treated.Still another need is for a system that infers genetic findings for anindividual in a reliable, cost effective, efficient and safe manner.

SUMMARY OF THE INVENTION

In one aspect of the present invention, a method and system in acomputing environment for inferring one or more DNA test results for aperson is provided. The system receives a query from another applicationseeking genetic findings for a person. The system inquires if the personhas the genetic findings. If not, the system automatically providesinferred genetic findings for the person. Further, the system calculatesthe inferred genetic findings for the person. The system may calculatethe inferred genetic findings based on the genetic findings of one ormore family members of the person. Semantic test results for the personmay be used to calculate inferred results for the person. The system mayalso calculate the inferred genetic findings using linkage analysis orhaplotype analysis. Finally, the system may calculate the inferredgenetic findings for the person by obtaining population geneticinformation for the person.

In another aspect, a method in a computer system for inferring DNA testresults for a person is provided. A request from a user for actualgenetic findings for one or more genes for a person is received. Themethod inquires whether the person has the genetic findings. If theperson does not have the genetic findings, the method automaticallyprovides inferred genetic findings for the person.

Further, the method calculates the inferred genetic findings for theperson. The method may calculate the inferred genetic findings based onthe genetic findings of one or more family members of the person.Semantic test results for the person may be used to calculate inferredresults for the person. The method may also calculate the inferredgenetic findings using linkage analysis or haplotype analysis. Finally,the method may calculate the inferred genetic findings for the person byobtaining population genetic information for the person

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The present invention is described in detail below with reference to theattached drawing figures, wherein:

FIG. 1 is a block diagram of a computing system environment suitable foruse in implementing the present invention;

FIGS. 2A and 2B are flow diagrams of a method and system for inferringgenetic findings in accordance with an embodiment of the presentinvention;

FIG. 3 is a diagram illustrating examples of modes of inheritance;

FIG. 4 is a diagram of an example of mitochondrial inheritance;

FIG. 5 is a diagram of an example of autosomal dominant inheritance; and

FIG. 6 is diagram of an example of a Y-linked inheritance.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides a method and system capable of inferringgenetic findings for a patient. FIG. 1 illustrates an example of asuitable medical information computing system environment 20 on whichthe invention may be implemented. The medical information computingsystem environment 20 is only one example of a suitable computingenvironment and is not intended to suggest any limitation as to thescope of use or functionality of the invention. Neither should thecomputing environment 20 be interpreted as having any dependency orrequirement relating to any one or combination of components illustratedin the exemplary environment 20.

The invention is operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well-known computing systems, environments, and/orconfigurations that may be suitable for use with the invention include,but are not limited to, personal computers, server computers, hand-heldor laptop devices, multiprocessor systems, microprocessor-based systems,set top boxes, programmable consumer electronics, network PCs,minicomputers, mainframe computers, distributed computing environmentsthat include any of the above systems or devices, and the like.

The invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include, but are notlimited to, routines, programs, objects, components, data structuresthat perform particular tasks or implement particular abstract datatypes. The invention may also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media, including memory storage devices.

With reference to FIG. 1, an exemplary medical information system forimplementing the invention includes a general purpose computing devicein the form of server 22. Components of server 22 may include, but arenot limited to, a processing unit, internal system memory, and asuitable system bus for coupling various system components, includingdatabase cluster 24 to the control server 22. The system bus may be anyof several types of bus structures, including a memory bus or memorycontroller, a peripheral bus, and a local bus using any of a variety ofbus architectures. By way of example, and not limitation, sucharchitectures include Industry Standard Architecture (ISA) bus, MicroChannel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronic Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus, also known as Mezzanine bus.

Server 22 typically includes therein or has access to a variety ofcomputer readable media, for instance, database cluster 24. Computerreadable media can be any available media that can be accessed by server22, and includes both volatile and nonvolatile media, removable andnonremovable media. By way of example, and not limitation, computerreadable media may comprise computer storage media and communicationmedia. Computer storage media includes both volatile and nonvolatile,removable and nonremovable media implemented in any method or technologyfor storage of information, such as computer readable instructions, datastructures, program modules or other data. Computer storage mediaincludes, but is not limited to, RAM, ROM, EEPROM, flash memory or othermemory technology, CD-ROM, digital versatile disks (DVD), or otheroptical disk storage, magnetic cassettes, magnetic tape, magnetic diskstorage, or other magnetic storage devices, or any other medium whichcan be used to store the desired information and which can be accessedby server 22. Communication media typically embodies computer readableinstructions, data structures, program modules, or other data in amodulated data signal, such as a carrier wave or other transportmechanism, and includes any information delivery media. The term“modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia includes wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media. Combinations of any of the above should also be includedwithin the scope of computer readable media.

The computer storage media, including database cluster 24, discussedabove and illustrated in FIG. 1, provide storage of computer readableinstructions, data structures, program modules, and other data forserver 22.

Server 22 may operate in a computer network 26 using logical connectionsto one or more remote computers 28. Remote computers 28 can be locatedat a variety of locations in a medical environment, for example, but notlimited to, clinical laboratories, hospitals, other inpatient settings,a clinician's office, ambulatory settings, medical billing and financialoffices, hospital administration, and home health care environment.Clinicians include, but are not limited to, the treating physician,specialists such as surgeons, radiologists and cardiologists, emergencymedical technicians, physician's assistants, nurse practitioners,nurses, nurse's aides, pharmacists, dieticians, microbiologists, and thelike. The remote computers may also be physically located innon-traditional medical care environments so that the entire health carecommunity is capable of integration on the network. Remote computers 28may be a personal computer, server, router, a network PC, a peer device,other common network node or the like, and may include some or all ofthe elements described above relative to server 22. Computer network 26may be a local area network (LAN) and/or a wide area network (WAN), butmay also include other networks. Such networking environments arecommonplace in offices, enterprise-wide computer networks, intranets andthe Internet. When utilized in a WAN networking environment, server 22may include a modem or other means for establishing communications overthe WAN, such as the Internet. In a networked environment, programmodules or portions thereof may be stored in server 22, or databasecluster 24, or on any of the remote computers 28. For example, and notlimitation, various application programs may reside on the memoryassociated with any one or all of remote computers 28. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

A user may enter commands and information into server 22 or convey thecommands and information to the server 22 via remote computers 28through input devices, such as keyboards, pointing devices, commonlyreferred to as a mouse, trackball, or touch pad. Other input devices mayinclude a microphone, satellite dish, scanner, or the like. Server 22and/or remote computers 28 may have any sort of display device, forinstance, a monitor. In addition to a monitor, server 22 and/orcomputers 28 may also include other peripheral output devices, such asspeakers and printers.

Although many other internal components of server 22 and computers 28are not shown, those of ordinary skill in the art will appreciate thatsuch components and their interconnection are well known. Accordingly,additional details concerning the internal construction of server 22 andcomputer 28 need not be disclosed in connection with the presentinvention.

Although the method and system are described as being implemented in aWINDOWS operating system operating in conjunction with an Internet basedsystem, one skilled in the art would recognize that the method andsystem can be implement in any system supporting the receipt andprocessing of clinical agent information or genetic findings.

The system of the present invention uses genetics based logic without auser actively seeking to apply genetics to the clinical situation. Inother words, the system provides a user with an unsolicited inference ofgenetic finding(s) for a person. With reference to FIG. 2A, in the firstembodiment of the present invention, a system and method are providedfor automatically inferring genetic findings for a person such as apatient. The system and method also utilizes inference information of apatient to infer genetic findings and the risk of an atypical clinicalevent (ACE).

Initially, at block 29, the system is queried. An application or modulein a healthcare information technology (HCIT) system may query thesystem of the present invention for actual or inferred genetic findingsfor a patient. Alternatively, the system may receive a query directlyfrom a user requesting actual genetic findings for a person. Forexample, when reviewing chest X-rays for a patient, a radiologistqueries the system to determine if there are actual genetic findings forcystic fibrosis for the patient.

An example of a request embedded in an application is a requestinitiated by a decision support rule that queries for genetic findings.For instance, a computerized physician order entry (CPOE) applicationmay have embedded logic to evaluate an electronic order to determinewhether or not the order may be contraindicated and/or associated withparticular genetic findings. The electronic order may be for a clinicalagent, a clinical event or any of a number of orderables known to thoseof skill in the art. Clinical agents as used herein include drugs,pharmaceuticals, nutriceuticals, foods, salves, dietary supplements andthe like. Clinical events include events such as lab tests, surgicalprocedures, therapies, orderable procedures, diagnoses, reflex, symptomsincoming results, scheduling events, documentation events, opening anelectronic chart, registering a patient and combinations thereof.

In one embodiment, once an order is input or other action is takenwithin the HCIT system, a table or database is searched to determine ifa clinical agent or event is associated with a genetic finding. Thetable includes a list of agents and events and any genetic findings thatmay be associated with each of the agents or events. As appreciated bythose of skill in the art, a single agent or event may have associationswith numerous genetic findings. Similarly, a genetic finding may haveassociations with more than one agent or event.

If the decision support rule determines that the clinical agent or eventis contraindicated or otherwise associated with one or more geneticfindings, the application queries the system of the present invention tocheck whether a patient has the genetic findings that may contraindicateor otherwise associated with the clinical agent or clinical event. Oneof skill in the art will appreciate that any variety of decision supportrules and applications may query the system of the present invention.The system may also be queried by a user for actual genetic findings forthe patient. For example, when reviewing chest X-rays for a patient, theradiologist queries the system to determine if there are actual geneticfindings for cystic fibrosis for the patient.

At block 30, the system obtains information regarding whether thepatient has genetic findings for which the query is directed. Forexample, the system may be queried to determine whether the patient hasany genetic findings for a particular gene, such as the cystic fibrosis.The system may also be queried to find a specific genetic finding (e.g.a particular mutation in a specific gene.)

In one embodiment, the system would access the patient's electronicmedical record to determine if the record contains the genetic findings.The genetic findings include genetic test results for any mutations of aparticular gene, such as deletions, additions, insertions, inversions,duplications and complex rearrangements and any other type of mutations.Genetic findings may also include DNA sequence information, analysis ofa polymorphic markers and phenotypic observations (such as blood type,PKU and other monogenetic traits with a well-understood basis).

In an alternative embodiment, a clinician checks to see if a patient hasthe genetic findings and enters a value(s) into the system to indicatethe patient does or does not have genetic findings queried for. If thepatient has the genetic findings, the system continues at block 31.

If the patient does not have the genetic findings, at block 32, thesystem determines whether inferred genetic findings for the patient areallowed. In one embodiment, the system searches an inference table, suchas Table 1 below, to determine whether or not inferred results areallowed for the particular genetic findings. If inferred geneticfindings are not allowed, the system continues at block 31. In oneembodiment, block 32 is bypassed.

If inferred results are allowed, the system obtains inferenceinformation at block 33. The inference information can include geneticinformation of family members, genetic findings for linked genes ormarkers, semantic information, population genetics information orcombinations thereof.

At block 34, the system utilizes the inference information to calculateinferred genetic findings for the patient. The system can calculate theinference in any variety of ways. For instance, the system could infergenetic findings for a patient based on the genetic findings for membersof the patient's family. The system utilizes genetic inheritanceprinciples to calculate inferred genetic findings for a patient usinggenetic findings for the patient's family members.

The system can calculate an inference based on information embedded in asemantic network describing biological information. For example, thesystem utilizes cytogenetic observations for the patient, such as theabsence of a chromosomal band, to calculate an inferred result. Forexample, cytogenetic testing reveals that a patient has a deletion inchromosome 4 that includes band 4q12.1. Based on this information, thesystem can infer that the patient is likely to missing one copy of geneA, described by the semantic network as being included in band 4q12.1.

The system may also calculate an inference using linkage analysis.Linkage analysis uses genetic maps that show the physical location ofgenes and markers related to one another. It is known that genes andmarkers located on the same chromosome and which are close to oneanother are likely to be co-inherited. Knowledge of the genotype of oneor more genes or genetic markers may also be used to make a reasonableestimation of the genotype of an adjacent gene when given the genotypeusing statistical algorithms and haplotype maps. This inferencecalculation utilizes genetic findings for members of the patient'sfamily, genetic findings for any linked genes or markers for members ofthe patient's family and genetic findings for any linked genes ormarkers for the patient.

The system may also infer results based upon genetic findings for anylinked genes or markers for the patient. In other words, the system mayperform a haplotype analysis for the patient and calculate inferredgenetic findings. To perform a haplotype analysis, genetic maps showingthe physical location of genes and markers related to one another andstatistical algorithms are used. For example, relative to three genesarranged linearly on a chromosome (A, B and C), a haplotype map mightindicate a person who has version 1 of gene A and version 2 of gene B is90% likely to have version 2 of gene C. On the other hand, the generalpopulation is only 2% likely to have version 2 of gene C.

The system may also calculate inferred genetic findings based onpopulation genetics information. The system of the present invention mayuse any combination of inference information to calculate an inferenceof genetic findings for a patient.

At block 35, the system informs the user of the inferred geneticfindings. The system may also inform whether the inferred geneticfindings indicate a high risk of an atypical event. An atypical event asused herein includes (a) adverse reactions to a clinical agent orclinical event and (b) reactions to the clinical agent or event thatresult in little or no benefit to the patient. If the system determinesthat there is little likelihood the patient has the genetic findingsassociated with an atypical event, the system can continue withoutnotifying the clinician of the inferred genetic finding and/or the risk.On the other hand, the system can communicate the inferred geneticfinding and/or any associated risks to the clinician. Alternatively, ifthe system determines there is a high likelihood that the patient hasthe genetic finding associated with an atypical event, the system maysend a message to the user or clinician informing them of the inferredresult at block at block 35.

The message can be communicated by a graphical display window indicatingthe probability that the patient may experience an atypical event. Themessage also may be communicated by alphanumeric page, e-mail or anyother form of automated communication.

Additional clinical action may be taken based on the inferred resultdetermined by the system. For example, the inferred result may berecorded in a central medical system into the patient's electronicmedical record, a confirmatory genetic test may be ordered for thepatient, the administration of the clinical action may be delayed orcanceled, additional therapy scheduled, an alternative agent may beselected, or the patient may be referred to a clinical counselor.Procedures may also be ordered for the patients based in the inferredgenetic finding. For instance, if the system has determined that apatient is 50% likely to have the mutant version of the BRCA1 gene thatincreases her risk of developing breast cancer, mammograms may bescheduled for the patient on a biyearly basis, rather than a yearlybasis, based on the inferred genetic finding.

With reference to FIG. 2B, another system and method are provided forinferring genetic findings for a patient is provided. At block 36, thesystem is queried. The system may be queried by another application foractual or inferred genetic findings for a particular patient.Alternatively, a user may directly query the system for actual geneticfindings. At block 38, the system determines whether the patient has thegenetic finding. If the patient has the genetic finding, the systemcontinues at block 39. If the patient does not have the genetic finding,at block 40, the system determines whether inferred genetic findings areallowed.

If inferred genetic findings are allowed, at block 42, the systemdetermines whether the patient has genetic findings for any genes orgenetic markers linked to genetic findings for which the query isdirected. In one embodiment, the system searches an inference table todetermine the maximum distance to search for related results or “MaxLOD”. Table 1 is an example of a portion of an inference table.

Chromo- Number of Mode of somal Max Inference Genera- Gene InheritanceLocation LOD Allowed tions CYP2D6 Autosomal Yes 2 RYR1 Autosomal 19q13.1100 cM No 0 CBP X linked X28  50 cM Yes 3

For example, if system has been queried to find any genetic findings forthe RYR1 gene, the system will search for genetic findings for linkedgenes or markers within 100 centimorgans from chromosome 19, arm q band13. Preferably, the system accesses a database containing personalinformation about the patient to obtain genetic findings for linkedgenes or genetic markers. The system may also include a databaserepresenting the genomic positions of genes and markers in order tosupport these calculations. If the system obtains genetic findings forlinked genes or genetic markers, the system can utilize this informationto perform a linkage analysis or haplotype analysis to calculateinferred genetic findings for the patient as described below. In oneembodiment, block 42 is bypassed.

At block 44, the system determines if the patient has semantic testresults for the genetic findings for the patient. If so, the system mayutilize this factor to calculated inferred genetic findings or may usethe semantic test results with other inference information to calculatean inferred genetic finding for the patient. In one embodiment, block 44is bypassed.

Next at block 46, the system identifies the appropriate traversalpattern for the genetic findings for which the query is directed. In oneembodiment, the system searches an inference table to obtain the mode ofinheritance for the genetic findings as shown above in Table 1. Based onthe mode of inheritance, the system determines which family members arewithin the traversal pattern and should be examined. With reference toFIG. 3, four modes of inheritance 60 are illustrated. A mitochondrialDNA mode of inheritance 62 leads to a traversal in which the femaleancestors of the family and their progeny and all progeny 64 of a femalepatient are examined. For male patients, only the female ancestors andthe female ancestors' progeny 66 are examined. As can be seen in FIG. 4,the female ancestors 84 of the female patient 82 and the ancestors'progeny 82, 90 and the progeny of the female patient 86, 88 areexamined.

With reference back to FIG. 3, an autosomal mode of inheritance 68 obeysclassical Mendelian inheritance and requests a traversal of all bloodrelatives 70. As can be seen in FIG. 5, all blood relatives 94 of thepatient 92 are examined.

With reference to FIG. 3, an X linked mode of inheritance 72 leads to atraversal in which female ancestors and all offspring of a male patientare examined. For a female patient, all female ancestors and theirmaternal descendents and all descendants of the female patient 74 areexamined.

A Y linked mode of inheritance 76 leads to a traversal in which onlymale relatives or descendants of a male patient 78 are examined. If thepatient is female, no family or descendants 80 are examined. As can beseen in FIG. 6, all male ancestors 98 of the male patient 96 and maleprogeny 100 of a male patient 96 are examined.

Referring again to FIG. 2B, at block 50, the system identifies familymember(s) of the patient who fall within the traversal pattern. In oneembodiment, a database having documentation of family relationships isaccessed. The number of generations of family members who are examinedmay be limited. In one embodiment, the system searches an inferencetable, such as Table 1, to determine the maximum number of generationsof family members who are examined for genetic findings.

At decision block 52, the system determines whether the family member(s)within the traversal pattern have the genetic findings. For example, thesystem obtains from a database the medical records of each family memberwho qualifies for the traversal. The medical records are checked todetermine whether or not the family member has the genetic findings.

If at decision block 52 the system determines that no family memberswithin the traversal pattern have the genetic findings, at block 45, thesystem determines if the patient had genetic findings for any linkedgenes or markers and if so performs a haplotype analysis at block 56. Asdiscussed above, a haplotype analysis uses maps and statisticalalgorithms to predict the most likely variant for the genetic findingbeing queried for.

If at block 45, the patient does not have genetic findings for anylinked genes the system obtains demographic information for the patientat block 48. At block 56 the system utilizes the demographic informationto calculate a population genetics inference. As known in the art and asset forth in the example that follows, gender, ethnic, and geographicdistribution information is indicative of genetic predisposition tocertain conditions. For instance, numerous studies have found that thefrequency of mutations in drug acetylation may vary among populations ofdifferent ethnicity and geographic origin. Meyer et al., MolecularMechanisms of Genetic Polymorphisms of Drug Metabolism, Annu. Rev.Pharmacol. Toxicol., 1997: 37: 269-295. By way of example, 40-70% ofthose in populations of European and North American descent displayacetylators of izoniazid, compared to only 10-30% of those from PacificAsian populations. Other genes have widely varying genotypicfrequencies. For example, mutated forms (or alleles) of one particulargene, CYP2D6, vary greatly between Caucasian, Asian, Black African, andEthiopian and Saudi Arabian populations. Ingelman-Sundberg et al,Polymorphic human cytochrome P450 enzymes: an opportunity forindividualized drug treatment, Trends. Pharmacol. Sci., 1999:20(8):342-349.

If at decision block 52, the system determines there are one or morefamily member(s) of the patient who have the genetic findings, thesystem determines whether those family member(s) also have geneticfindings for linked genes or markers at block 54. In one embodiment,block 54 is bypassed.

Next at block 56, the system calculates an inferred result for thepatient. The inferred results are calculated using the genetic findingsfor family member(s). The system uses Pro Form a logic, Bayesiancalculations or other statistical or algorithmic approaches to inferresults for the patient. The system utilizes the familial geneticfindings to infer a genetic finding for the patient.

The system may also calculate the likelihood the patient has a genevariant indicative of an atypical event based on the inferred geneticfinding. In one embodiment, the system searches the polymorphism/risktable or database for the inferred genetic finding and determines ifthere are any risks associated with the inferred genetic finding.

The system performs a linkage analysis using genetic maps that show thephysical location of genes relative to one another and statisticalalgorithms. As discussed above, it is known that genes or markerslocated on the same chromosome and are close to one another are likelyto be co-inherited.

In this embodiment, the inferred results are calculated by utilizinggenetic findings for linked genes or markers for the patient, geneticfindings for linked genes or markers for one or more family members, andthe genetic findings for one or more family members. Linkage analysismay be performed using Quantitative Trait Loci (QTL) analysis or anyother approach known to those skilled in the art.

At block 58, the system informs the user of the inferred geneticfinding. Again, the system may also inform whether the inferred resultindicates a high risk of an atypical event. If the system determinesthere is a high likelihood that the patient has the genetic findingsassociated with an atypical event, the system may send a message to theuser or clinician informing them of the inferred result at block atblock 58. The message can be communicated by a graphical display windowindicating the probability that the patient may experience an atypicalevent. The message also may be communicated by alphanumeric page, e-mailor any other form of automated communication. This message can alsoprovide the user with information about the mode of inference that wasapplied.

Additional clinical action may be taken based on the inferred resultdetermined by the system. For example, the inferred result may berecorded in a central medical system by appending the result into thepatient's electronic medical record, a confirmatory genetic test may beordered for the patient, the administration of the clinical action maybe delayed or canceled, additional therapy scheduled, an alternativeagent may be selected, or the patient may be referred to a clinicalcounselor.

In operation, by way of example, a child is seriously injured whileplaying at a friend's home. He is rushed to the local emergency room.During the preparations for surgery, the attending surgeon chooses aprotocol that would involve the use of halothane. A decision supportrule determines that a mutation in the RYR1 gene can lead to malignanthyperthemia in response to halothane. With reference to FIG. 2B at block36, the system is queried for an actual genetic finding for the RYR1gene for the patient. At block 38, the system determines that thepatient does not have any genetic findings for the RYR1 gene. Blocks 40,42 and 44 are bypassed. At block 46, the system determines that thetraversal pattern for the gene is autosomal dominant and at block 52 thesystem searches to determine whether any of the patient's family hadbeen tested for this trait. The system determines at block 52 that thechild's father had been tested and found to have a mutation in the RYR1gene. Block 54 is bypassed. At block 56, the system calculates aninferred genetic finding for the patient and determines that there is a50% chance that the child has the mutated gene that leads to malignanthyperthemia in response to halothane. At block 58, the system informs ofthe risk by notifying the surgeon with a page of the inferred result andwarning her that there is a 50% chance that child would have a severereaction to halothane. In response to the information, the surgeonchooses an alternative protocol and the child recovers fully from hisinjury. Subsequently, a follow-up genetic test is ordered to confirmwhether or not the child is actually at risk.

In a second example, a woman is seen by her gynecologist and requestsestrogen therapy as a means to help her with menopause. A decisionsupport rule determines that a mutation in the Factor V gene incombination estrogen therapy increases the risk of thrombosis andqueries the system at block 36 for actual genetic findings for theFactor V gene for the patient. At decision block 38, the systemdetermines the patient does not have genetic findings for the Factor Vgene. Block 40 is bypassed. However, at block 42, the system determinesthat the patient has genetic findings for a mutation in the CMT1B genefor Charcot-Marie-Tooth disease. The CMT1B is located near the Factor Vgene and is a linked gene. Block 44 is bypassed. At block 46, the systemdetermines the traversal pattern for the Factor V gene is autosomal. Atblock 50, the system determines the family members of the patient withinthe traversal pattern. At block 52 determines that the patient's motherhas a mutated Factor V gene. At block 54, the system determines that thepatient's mother has Charcot-Marie-Tooth disease. At block 56, thesystem utilizes the patient's genetic findings for the CMT1B gene, themother's genetic findings for the mutated Factor V gene and the mother'sphenotypic information for Charcot-Marie-Tooth disease to calculate aninferred result by performing linkage analysis. The system infers thatthere is a very strong likelihood that the patient has a mutated FactorV gene and is at significant risk of thrombosis if she began estrogentherapy. At block 58 the system informs the gynecologist of the inferredresult by sending a pop-up warning.

In another aspect of the invention, the system may determine the risksassociated with a specific genetic test result input. For example,genetic findings for one or more family members of a patient may beinput. Next, the system determines that few risks are associated withgenetic findings for the one or more family members. For example, thesystem could add a comment to an integrated electronic medical recordthat no risks were determined for the genetic findings of the patient'sfamily members. Next, the user could be provided with interpretation ofthe genetic findings. In this case, the user would be provided with anindication that the genetic findings were not associated with any knownrisks.

Conversely, if genetic risks are known for the relevant geneticfindings, a list of potential risks is generated. From this list, a listof agents or events that are associated with the mutation indicated bythe genetic test result is generated. Further, the system may determinethat the patient has been exposed to the agent or events or mayprospectively be exposed to the agent or events. If the patient has beenexposed to the agent, the system generates an automated clinicalresponse associated with the high risk. This response may includesuspension or cancellation of the order, placing an alternative order,paging the ordering clinician, ordering follow-up tests, or schedulingcounseling for the patient. Once all of the agents or events areconsidered, the user is provided with an automated interpretation of theresults. In this case, the interpretation would indicate to the userthat certain clinical agents or events should be avoided due to thegenetic predisposition to an atypical clinical reaction or that thepatient should be tested for the specific gene.

In another embodiment, the completion of a genetic test result for apatient may be used to trigger automated updates to the medical recordsof family members of the patient. In this embodiment, the patientrecords of the family members are updated to reflect the probability ofthe family member sharing the same genetic findings as the patient. Inthis embodiment, the system would use the genetic findings of thepatient along with genetic findings of any others within the family toinfer genetic findings for the family member.

Since the system may be integrated with architectures spanning thehealthcare organization, the system will operate to manage the riskassociated with clinical agents without creating inefficiencies. Thesystem and method of the present invention seamlessly integrates complexgenetic information and unchanging genetic information into an overallhealthcare system. The invention makes genetics-based logic widelyavailable for use, intended or not, by the non-expert in genetics. Thisinvention adds value to clinical information systems by maximizing theutility of genetic findings. By integrating unchanging hereditaryinformation with newfound knowledge associating this information tocertain clinical agents and events, the system will allow the caregiverto appreciate the risks that are not readily apparent from the symptomsof the patient or associated with the particular agent.

Moreover, in the preferred embodiment, the system and method isimplemented into a comprehensive automated healthcare system within thecontext of existing storage media and clinical processes. As mentionedabove, the demographic information, familial genetic information andindividualized genetic information may be stored in an electronicmedical record.

As mentioned at the outset, consideration of the hereditary geneticinformation may be incorporated in the physician's standard of care, asthe implications of the information become widely known. Absent thesystem and method of the present invention, it would be burdensome andinefficient for non-expert physicians to consider this important geneticinformation. The inclusion of this information in the electronic medicalrecord or other permanent data structure allows physicians to makedecisions based on the latest understandings of genetic information byaccessing the updated databases.

Although the invention has been described with reference to thepreferred embodiment illustrated in the attached drawing figures, it isnoted that substitutions may be made and equivalents employed hereinwithout departing form the scope of the invention as recited in theclaims. For example, additional steps may be added and steps omittedwithout departing from the scope of the invention.

1. A method, implemented by a server, for determining and presenting thelikelihood a person has a mutated form of a gene, the method comprising:receiving an electronic order from a clinician for at least one clinicalagent for a person; in response to receiving the electronic order forthe at least one clinical agent, searching a table to determine whetherthe at least one clinical agent is associated with a gene, the tablelisting a plurality of genes associated with a plurality of clinicalagents and clinical events; upon searching the table, querying a firstdatabase to determine whether the person has one or more genetic testresults for the gene determined to be associated with the at least oneclinical agent; when the first database indicates that the person doesnot have the one or more genetic test results for the gene, identifyingan appropriate traversal pattern of the gene within a family of theperson identified, wherein the traversal pattern of the gene isidentified by a process comprising: (a) accessing an inference tablethat associates genes with a plurality of modes of inheritance; (b)querying the inference table with the gene to select a mode ofinheritance that corresponds with the gene; and (c) using the selectedmode of inheritance to determine the traversal pattern of the genewithin the person's family; identifying at least one family memberwithin the identified traversal pattern of the person's family forinspection; querying a second database to determine whether at least oneidentified family member of the person within the traversal pattern hasone or more genetic test results related to the gene; when the at leastone identified family member has the one or more genetic test resultsfor the gene, utilizing the one or more genetic test results toautomatically calculate a likelihood the person exhibits a mutated formof the gene; and presenting the calculated likelihood the person has amutated form of the gene to the clinician.
 2. The method of claim 1,wherein the second database comprises an electronic medical record foreach family member stored within a comprehensive healthcare system. 3.The method of claim 1, further comprising: when no family member withinthe identified traversal pattern has the one or more genetic testresults for the gene, inquiring whether the at least one identifiedfamily member of the person within the traversal pattern has one or moregenetic markers related to the gene; and when the at least oneidentified family member of the person within the traversal pattern hasone or more genetic markers related to the gene, performing a haplotypeanalysis utilizing the one or more genetic markers to predict alikelihood that the person expresses a mutated form of the gene.
 4. Themethod of claim 1, further comprising: when no family member within theidentified traversal pattern has the one or more genetic test resultsfor the gene, determining that the person has one or more geneticfindings for one or more linked genes associated with the gene; andutilizing the one or more linked genes of the person to calculate thelikelihood the person has a mutated form of the gene.
 5. The method ofclaim 1, further comprising providing one or more non-transitorycomputer storage media having computer executable instructions to carryout at least one step of the method.
 6. The method of claim 1, furthercomprising: determining whether the mutated form of the gene is a genevariant indicative of an atypical event.
 7. The method of claim 6,wherein if the mutated form of the gene is a gene variant indicative ofan atypical event, presenting an alert to a user.
 8. The method of claim1, wherein the mode of inheritance is selected from one of amitochondrial DNA mode of inheritance, an X-linked mode of inheritance,a Mendelian mode of inheritance, and a Y-linked mode of inheritance. 9.The method of claim 1 wherein said first and second databases are thesame database.
 10. A computer system for determining and presenting thelikelihood a person has a mutated form of a gene, the computer systemcomprising: a server configured to execute: (1) a receiving module forreceiving an electronic order for at least one clinical agent for aperson from a clinician, wherein the electronic order does not indicatea request to use genetic techniques to characterize the person'sresponse to the at least one clinical agent for the person; (2) adetermining module for: (a) determining, in response to receiving theelectronic order, whether the at least one clinical agent is associatedwith a gene, and (b) searching an inference table to determine a maximumdistance away from the gene to search for genetic findings of linkedgenes for the person; (3) a first querying module for querying, inresponse to the electronic order, a first database to determine if theperson has one or more genetic test results for the gene if the at leastone clinical agent is associated with one or more genetic test results;(4) an obtaining module for, when the person does not have the one ormore genetic test results for the gene, identifying an appropriatetraversal pattern of the gene within a family of the person, wherein thetraversal pattern of the gene is identified by a process comprising: (a)accessing an inference table that associates genes with a plurality ofmodes of inheritance; (b) querying the inference table with the gene toselect a mode of inheritance that corresponds with the gene; (c) usingthe selected mode of inheritance to determine the traversal pattern ofthe gene within the person's family; and (d) identifying at least onefamily member related to the person within the identified traversalpattern of the person's family for inspection; (5) a second queryingmodule for querying a second database to determine whether at least oneidentified family member of the person within the traversal pattern hasone or more genetic test results for the gene; (6) a utilizing modulefor utilizing the one or more genetic test results of the at least onefamily member to automatically calculate a likelihood the person has amutated form of the gene if at least one of the family members hasgenetic test results for the gene; and (7) a presenting module forpresenting the calculated likelihood the person has a mutated form ofthe gene to the clinician without solicitation from the clinician forthe calculated likelihood.
 11. The system of claim 10, wherein thesecond database comprises an electronic medical record for each familymember stored within a comprehensive healthcare system.
 12. The systemof claim 10, wherein the second querying module determines the mode ofinheritance has one or more genetic markers related to the gene.
 13. Thesystem of claim 12, wherein the utilizing module utilizing the one ormore genetic markers of at least one family member of the person tocalculate the likelihood the person has a mutated form of the gene. 14.The system of claim 10, wherein the first database comprises anelectronic medical record for the person.
 15. The system of claim 10,further comprising: a determining module for determining whether themutated form of the gene is a gene variant indicative of an atypicalevent.
 16. The system of claim 15, wherein if the mutated form of thegene is a gene variant indicative of an atypical event, the presentingmodule presents an alert to a user.
 17. The system of claim 10, whereinthe mode of inheritance is selected from one of a mitochondrial DNA modeof inheritance, an X-linked mode of inheritance, a Mendelian mode ofinheritance, and a Y-linked mode of inheritance.
 18. A method,implemented by a server, for determining and presenting the likelihood aperson has a mutated form of a gene, the method comprising: receivingfrom a clinician an order for a medication for a person, wherein theorder does not indicate a request to use genetic techniques tocharacterize the person's response to the medication; in response toreceiving the clinician order for medication, determining whether theorder for medication is associated with a gene; querying a firstdatabase to determine if the person has one or more genetic test resultsfor the gene; when the first database indicates that the person does nothave the one or more genetic test results for the gene, identifying anappropriate traversal pattern of the gene within a family of the personidentified, wherein the traversal pattern of the gene is identified by aprocess comprising: (a) accessing an inference table that associatesgenes with a plurality of modes of inheritance; (b) querying theinference table with the gene to select a mode of inheritance thatcorresponds with the gene; and (c) using the selected mode ofinheritance to determine the traversal pattern of the gene within theperson's family; identifying at least one family member within theidentified traversal pattern of the person's family for inspection;querying a second database to determine whether at least one identifiedfamily member of the person within the traversal pattern has one or moregenetic test results related to the gene; when the at least oneidentified family member has the one or more genetic test results forthe gene, utilizing the one or more genetic test results toautomatically calculate a likelihood the person exhibits a mutated formof the; and when the at least one identified family member does not havethe one or more genetic test results for the gene, automaticallydetermining whether inferred results are allowed for the gene; wheninferred results are allowed, automatically calculating an inferredfinding that the person has a mutated form of the gene based, in part,on one or more genetic findings expressed by the person, wherein the oneor more genetic findings include markers linked to the gene; andoutputting the inferred finding to a display for presentation in auser-readable format within a graphical user interface (GUI).
 19. Themethod of claim 18, further comprising one or more non-transitorycomputer storage media having computer executable instructons to carryout at least one step of the method.
 20. The method of claim 18, furthercomprising: determining whether the mutated form of the gene is a genevariant indicative of an atypical event.
 21. The method of claim 20,wherein if the mutated form of the gene is a gene variant indicative ofan atypical event, presenting an alert to a user.